Unphysical behaviour of the Hinde potential energy surface: Implications for the calculation of dissociation rate coefficients of H2 + H2
Bibliographic record
Abstract
Molecular hydrogen is the dominant molecular species present in the interstellar medium and has an important role in the cooling of shocks that are associated with star formation. The two mechanisms of cooling are collisional excitation followed by quadrupole emission and collisional dissociation. Modelling the role of dissociation in this cooling needs detailed information on the state specific dissociation rate coefficients. The initial goal of this research was to compare the trajectory outcomes on the Hinde potential energy surface (PES) with those on the BMKP2 PES to assess whether it is required to do extensive and more expensive calculations to determine state specific rate coefficients for dissociation of H2 + H2 to supersede those previously determined with the BMKP2 surface. A phenomenon of double dissociation was unexpectedly identified within the Hinde PES, despite the absence of sufficient energy for such an occurrence. These results prompted a comprehensive analysis of the Hinde PES, which in turn involved an exploration of the regions that exhibit unphysical behavior. This detailed examination unveiled problematic aspects of the potential energy surface. As a result of this, it has been determined that the Hinde PES is unsuitable for calculating dissociation rate coefficients for H2 + H2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".